Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data

Advanced Carriers for Precise Delivery and Therapeutic Mechanisms of Traditional Chinese Medicines: Integrating Spatial Multi-Omics and Delivery Visualization

The complex composition of traditional Chinese medicines (TCMs) has posed challenges for in-depth study and global application, despite their abundance of bioactive compounds that make them valuable resources for disease treatment. To overcome these obstacles, it is essential to modernize TCMs by focusing on precise disease treatment. This involves elucidating the structure-activity relationships within their complex compositions, ensuring accurate in vivo delivery, and monitoring the delivery process. This review discusses the research progress of TCMs in precision disease treatment from three perspectives: spatial multi-omics technology for precision therapeutic activity, carrier systems for precise in vivo delivery, and medical imaging technology for visualizing the delivery process. The aim is to establish a novel research paradigm that advances the precision therapy of TCMs.

WCSGNet: a graph neural network approach using weighted cell-specific networks for cell-type annotation in scRNA-seq

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for understanding cellular heterogeneity, providing unprecedented resolution in molecular regulation analysis. Existing supervised learning approaches for cell type annotation primarily utilize gene expression profiles from scRNA-seq data. Although some methods incorporated gene interaction network information, they fail to use cell-specific gene association networks. This limitation overlooks the unique gene interaction patterns within individual cells, potentially compromising the accuracy of cell type classification. We introduce WCSGNet, a graph neural network-based algorithm for automatic cell-type annotation that leverages Weighted Cell-Specific Networks (WCSNs). These networks are constructed based on highly variable genes and inherently capture both gene expression patterns and gene association network structure features. Extensive experimental validation demonstrates that WCSGNet consistently achieves superior cell type classification performance, ranking among the top-performing methods while maintaining robust stability across diverse datasets. Notably, WCSGNet exhibits a distinct advantage in handling imbalanced datasets, outperforming existing methods in these challenging scenarios. All datasets and codes for reproducing this work were deposited in a GitHub repository (https://github.com/Yi-ellen/WCSGNet).

Atlas of multilineage stem cell differentiation reveals TMEM88 as a developmental regulator of blood pressure

Pluripotent stem cells provide a scalable approach to analyse molecular regulation of cell differentiation across developmental lineages. Here, we engineer barcoded induced pluripotent stem cells to generate an atlas of multilineage differentiation from pluripotency, encompassing an eight-day time course with modulation of WNT, BMP, and VEGF signalling pathways. Annotation of in vitro cell types with reference to in vivo development reveals diverse mesendoderm lineage cell types including lateral plate and paraxial mesoderm, neural crest, and primitive gut. Interrogation of temporal and signalling-specific gene expression in this atlas, evaluated against cell type-specific gene expression in human complex trait data highlights the WNT-inhibitor gene TMEM88 as a regulator of mesendodermal lineages influencing cardiovascular and anthropometric traits. Genetic TMEM88 loss of function models show impaired differentiation of endodermal and mesodermal derivatives in vitro and dysregulated arterial blood pressure in vivo. Together, this study provides an atlas of multilineage stem cell differentiation and analysis pipelines to dissect genetic determinants of mammalian developmental physiology.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

scTab: Scaling cross-tissue single-cell annotation models

Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million cells). In this context, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales both in terms of training dataset size and model size. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets and demonstrate the benefits of using deep learning methods in this paradigm.

scAnnoX: an R package integrating multiple public tools for single-cell annotation

Background

Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking.

Methods

This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms.

Results

The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.

Integrative analysis identifies cancer cell-intrinsic RARRES1 as a predictor of prognosis and immune response in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with poor prognosis and limited treatment options. Although immune checkpoint inhibitors (ICIs) have been proven to improve outcomes in TNBC patients, the potential mechanisms and markers that determine the therapeutic response to ICIs remains uncertain. Revealing the relationship and interaction between cancer cells and tumor microenvironment (TME) could be helpful in predicting treatment efficacy and developing novel therapeutic agents. By analyzing single-cell RNA sequencing dataset, we comprehensively profiled cell types and subpopulations as well as identified their signatures in the TME of TNBC. We also proposed a method for quantitatively assessment of the TME immune profile and provided a framework for identifying cancer cell-intrinsic features associated with TME through integrated analysis. Using integrative analyses, RARRES1 was identified as a TME-associated gene, whose expression was positively correlated with prognosis and response to ICIs in TNBC. In conclusion, this study characterized the heterogeneity of cellular components in TME of TNBC patients, and brought new insights into the relationship between cancer cells and TME. In addition, RARRES1 was identified as a potential predictor of prognosis and response to ICIs in TNBC.

Uncertainty-aware single-cell annotation with a hierarchical reject option

MOTIVATION

Automatic cell type annotation methods assign cell type labels to new datasets by extracting relationships from a reference RNA-seq dataset. However, due to the limited resolution of gene expression features, there is always uncertainty present in the label assignment. To enhance the reliability and robustness of annotation, most machine learning methods address this uncertainty by providing a full reject option, i.e. when the predicted confidence score of a cell type label falls below a user-defined threshold, no label is assigned and no prediction is made. As a better alternative, some methods deploy hierarchical models and consider a so-called partial rejection by returning internal nodes of the hierarchy as label assignment. However, because a detailed experimental analysis of various rejection approaches is missing in the literature, there is currently no consensus on best practices.

RESULTS

We evaluate three annotation approaches (i) full rejection, (ii) partial rejection, and (iii) no rejection for both flat and hierarchical probabilistic classifiers. Our findings indicate that hierarchical classifiers are superior when rejection is applied, with partial rejection being the preferred rejection approach, as it preserves a significant amount of label information. For optimal rejection implementation, the rejection threshold should be determined through careful examination of a method's rejection behavior. Without rejection, flat and hierarchical annotation perform equally well, as long as the cell type hierarchy accurately captures transcriptomic relationships.

AVAILABILITY AND IMPLEMENTATION

Code is freely available at https://github.com/Latheuni/Hierarchical_reject and https://doi.org/10.5281/zenodo.10697468.

ProtoCell4P: an explainable prototype-based neural network for patient classification using single-cell RNA-seq

MOTIVATION

The rapid advance in single-cell RNA sequencing (scRNA-seq) technology over the past decade has provided a rich resource of gene expression profiles of single cells measured on patients, facilitating the study of many biological questions at the single-cell level. One intriguing research is to study the single cells which play critical roles in the phenotypes of patients, which has the potential to identify those cells and genes driving the disease phenotypes. To this end, deep learning models are expected to well encode the single-cell information and achieve precise prediction of patients' phenotypes using scRNA-seq data. However, we are facing critical challenges in designing deep learning models for classifying patient samples due to (i) the samples collected in the same dataset contain a variable number of cells-some samples might only have hundreds of cells sequenced while others could have thousands of cells, and (ii) the number of samples available is typically small and the expression profile of each cell is noisy and extremely high-dimensional. Moreover, the black-box nature of existing deep learning models makes it difficult for the researchers to interpret the models and extract useful knowledge from them.

RESULTS

We propose a prototype-based and cell-informed model for patient phenotype classification, termed ProtoCell4P, that can alleviate problems of the sample scarcity and the diverse number of cells by leveraging the cell knowledge with representatives of cells (called prototypes), and precisely classify the patients by adaptively incorporating information from different cells. Moreover, this classification process can be explicitly interpreted by identifying the key cells for decision making and by further summarizing the knowledge of cell types to unravel the biological nature of the classification. Our approach is explainable at the single-cell resolution which can identify the key cells in each patient's classification. The experimental results demonstrate that our proposed method can effectively deal with patient classifications using single-cell data and outperforms the existing approaches. Furthermore, our approach is able to uncover the association between cell types and biological classes of interest from a data-driven perspective.

AVAILABILITY AND IMPLEMENTATION

https://github.com/Teddy-XiongGZ/ProtoCell4P.

scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings

Single-cell transcriptomics is rapidly advancing our understanding of the composition of complex tissues and biological cells, and single-cell RNA sequencing (scRNA-seq) holds great potential for identifying and characterizing the cell composition of complex tissues. Cell type identification by analyzing scRNA-seq data is mostly limited by time-consuming and irreproducible manual annotation. As scRNA-seq technology scales to thousands of cells per experiment, the exponential increase in the number of cell samples makes manual annotation more difficult. On the other hand, the sparsity of gene transcriptome data remains a major challenge. This paper applied the idea of the transformer to single-cell classification tasks based on scRNA-seq data. We propose scTransSort, a cell-type annotation method pretrained with single-cell transcriptomics data. The scTransSort incorporates a method of representing genes as gene expression embedding blocks to reduce the sparsity of data used for cell type identification and reduce the computational complexity. The feature of scTransSort is that its implementation of intelligent information extraction for unordered data, automatically extracting valid features of cell types without the need for manually labeled features and additional references. In experiments on cells from 35 human and 26 mouse tissues, scTransSort successfully elucidated its high accuracy and high performance for cell type identification, and demonstrated its own high robustness and generalization ability.

Evaluation of single-cell RNAseq labelling algorithms using cancer datasets

Single-cell RNA sequencing (scRNA-seq) clustering and labelling methods are used to determine precise cellular composition of tissue samples. Automated labelling methods rely on either unsupervised, cluster-based approaches or supervised, cell-based approaches to identify cell types. The high complexity of cancer poses a unique challenge, as tumor microenvironments are often composed of diverse cell subpopulations with unique functional effects that may lead to disease progression, metastasis and treatment resistance. Here, we assess 17 cell-based and 9 cluster-based scRNA-seq labelling algorithms using 8 cancer datasets, providing a comprehensive large-scale assessment of such methods in a cancer-specific context. Using several performance metrics, we show that cell-based methods generally achieved higher performance and were faster compared to cluster-based methods. Cluster-based methods more successfully labelled non-malignant cell types, likely because of a lack of gene signatures for relevant malignant cell subpopulations. Larger cell numbers present in some cell types in training data positively impacted prediction scores for cell-based methods. Finally, we examined which methods performed favorably when trained and tested on separate patient cohorts in scenarios similar to clinical applications, and which were able to accurately label particularly small or under-represented cell populations in the given datasets. We conclude that scPred and SVM show the best overall performances with cancer-specific data and provide further suggestions for algorithm selection. Our analysis pipeline for assessing the performance of cell type labelling algorithms is available in https://github.com/shooshtarilab/scRNAseq-Automated-Cell-Type-Labelling.